Sentimental Analysis, leveraging RWE for understanding the safety profile

by simrana fathima

Over the past few decades, product Safety across the sectors like, Pharmacovigilance, Cosmetovigilance, Medical device vigilance and vigilance of nutraceuticals has evolved significantly. New approach towards patient safety have been encouraged. In the meantime, the digital era elevates the experience of a product by giving real time experience from the user. Social media which was used for sharing moments is now taking a new turn where all the products purchased are reviewed, rated and commented on.

These reviews and comments may be in colloquial, creative phases, idiomatic expressions or in hashtags. This data, when investigated, might be of great use for creating and managing the safety profile of a product, as they may contain safety-related concerns, but prove to be complex data source.

Manually sifting through millions of online reviews and forum discussions is practically impossible. This is where sentimental analysis will be very efficient as it can classify the reviews into positive, negative and neutral category.

The sentimental analysis can be approached to process unstructured online feedback into meaningful insights. Data from various e-commerce websites, patient forums, social media channels and other review platform where the products are purchased and reviewed can be collected by web scrapping. This data needs preprocessing to remove the duplicates and redundant reviews, to get the appropriate data to analyse the risks associated with products.

Sentiment analysis helps filter massive volumes of e-commerce or social media reviews highlighting safety-related complaints and potential hazards making it a cost-effective, real-time tool for protecting consumers and improving regulatory compliance.

 Steps for Pre-processing:

Data Processing: addressing incomplete textual entries and removing redundancies by applying de-duplication techniques such as exact string matching for identical reviews and entity resolution for repeated usernames, thereby ensuring a clean and reliable corpus.

Structuring Unorganized Data: Transforming unstructured online reviews into a structured, tabular format through techniques such as text parsing, tokenization, metadata extraction, and feature engineering, enabling their systematic organization into CSVs, relational databases or data frames for downstream analysis.

Data Tagging: Annotating review text with critical attributes such as Adverse Event (AE) terminology, demographic variables, geographic identifiers, and other contextual metadata through techniques like named entity recognition (NER), ontology mapping, and rule-based classification to enable structured analysis.

 AI-driven Categorization: Leveraging natural language processing (NLP) and machine learning techniques to classify feedback into sentiment categories (positive, negative, neutral) as well as domain-specific safety classes, including Adverse Events (AEs), Product Quality Complaints (PQCs), or hybrid instances (AE + PQC), thereby enabling automated, context-aware review segmentation.

Following categorization, the subsequent step involves validation, which can be achieved by refining the prompt engineering strategies used for classification, coupled with techniques such as cross-validation, human-in-the-loop review, and active learning, before rerunning the data to ensure robustness and accuracy. The validated dataset can then be subjected to advanced NLP methods to identify latent patterns. These patterns may subsequently be assessed through signal detection methodologies, including disproportionality analysis (PRR, ROR) and Bayesian inference models.

These are few steps which can be adopted to uncover early signals that might otherwise go unnoticed in traditional reporting systems. Sentimental analysis can act as a primary indicator for potential crisis, allowing faster intervention even before it occurs.

Benefits of Sentiment Analysis in Safety Monitoring

• Gaining safety and other valuable insights about the product.
• Early identification and identification of rare safety signals.
• Guides regulatory and manufacturer decisions.
• Evaluate competing products comparison in terms of effectiveness and safety.
• Vigilance across multiple segments like Cosmetics, Nutraceuticals, Herbal medicine, Pharmaceuticals.

By tapping into real-world evidence (RWE), organizations can identify safety signals earlier, act faster, and build greater trust with consumers and regulators alike.

Applications Across Industries

• Pharmaceuticals: Early recognition of adverse events beyond traditional reporting channels.
• Cosmetics & Nutraceuticals: Detecting product quality issues and consumer concerns in real time.
• Medical Devices: Identifying safety complaints or usability challenges through user feedback.

Key Challenges in Sentiment Analysis for Pharmacovigilance

Several challenges may arise during the sentiment analysis process, including:

• Restricted Data Access: Limited or restricted API availability for extracting and scraping online reviews.
• Signal Differentiation: Difficulty in distinguishing true adverse events from indications, comorbidities, or patient history references.
• Linguistic Complexity: The use of hashtags, idiomatic expressions, creative phrasing, and sarcasm can mislead models, requiring advanced NLP techniques to accurately interpret and classify such data.
• Quality Control: Post-categorization manual quality checks remain resource-intensive and challenging, underscoring the need for semi-automated or human-in-the-loop validation approaches.

Conclusion

Sentiment analysis, when combined with real-world evidence, offers a powerful way to strengthen pharmacovigilance. It allows organizations to detect early safety signals, gain a deeper understanding of patient experiences, and enhance decision-making at both regulatory and manufacturer levels.

Although challenges such as data access, language nuances, and quality control remain, the potential benefits outweigh the hurdles. With continuous refinement and validation, this approach can provide valuable insights into patient experiences, support early detection of risks.

Ultimately, adopting this approach will not only strengthen pharmacovigilance but also ensure that patients are better protected, products are improved based on genuine feedback, and trust in healthcare systems is reinforced.